Conventional data centers are known to include a number of sensors positioned to detect various conditions at locations of interest in the data centers. The conditions include, for example, temperature, pressure, vibration, humidity, energy consumption, and airflow direction and velocity. The locations of interest have included inlets and outlets of electronics cabinets, inlets and outlets of air conditioning units, and outlets of ventilation tiles.
The sensors have also been networked to a database system to enable constant streaming of data to the database system. The database system often aggregates the streaming data, for instance, to monitor the network of sensors. Conventional database systems often receive massive amounts of streaming data from the sensors, which typically make the database systems prohibitively slow. These database systems employ threshold-based monitoring techniques to detect anomalies in the data center, and therefore do not provide real-time notification of events to enable proactive maintenance of the data center. Furthermore, while the database systems are often able to identify particular sensors and particular sensor types, they often do not provide information regarding the scope and location of detected anomalies. Conventional database systems are therefore unable to provide users or other applications immediate, real-time responses to anomalies in the data collected by the sensors. This problem is further exacerbated by recent trends to increase the size of data centers and thus, the number of sensors employed to detect conditions in the data centers.
It would thus be beneficial to have the capability to quickly and automatically identify events or anomalies in an environment containing a sensor network.